class_docstr: Randomly apply some photometric transforms to an image.
  This was used in PIRL - https://arxiv.org/abs/1912.01991
  
  The photometric transforms applied includes:
      AutoContrast, RandomPosterize, RandomSharpness, RandomSolarize
docstr: Instantiates ImgPilRandomPhotometric from configuration.
  
  Args:
      config (Dict): arguments for for the transform
  
  Returns:
      ImgPilRandomPhotometric instance.
docstr_tok: ["Instantiates","ImgPilRandomPhotometric","from","configuration",".","Args",":","config","(","Dict",")",":","arguments","for","for","the","transform","Returns",":","ImgPilRandomPhotometric","instance","."]
code: def from_config(
      cls, config: Dict[str, Any]
  ) -> "ImgPilRandomPhotometric":
      """
      Instantiates ImgPilRandomPhotometric from configuration.
  
      Args:
          config (Dict): arguments for for the transform
  
      Returns:
          ImgPilRandomPhotometric instance.
      """
      p = config.get("p", 0.66)
      return cls(p=p)
  
file_path: vissl/vissl/data/ssl_transforms/img_pil_random_photometric.py
imports: import logging
  from typing import Any, Dict
  import torchvision.transforms
  from classy_vision.dataset.transforms import register_transform
  from classy_vision.dataset.transforms.classy_transform import ClassyTransform
  from vissl.data.ssl_transforms.pil_photometric_transforms_lib import AutoContrastTransform, RandomPosterizeTransform, RandomSharpnessTransform, RandomSolarizeTransform
label_desc: Use image node_id 3 for calling the ImgPilRandomPhotometric obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: cls